Abstract

Nonuniform exposures often affect imaging systems, e.g., owing to vignetting. Moreover, the sensor’s radiometric response may be nonlinear. These characteristics hinder photometric measurements. They are particularly annoying in image mosaicking, in which images are stitched to enhance the field of view. Mosaics suffer from seams stemming from radiometric inconsistencies between raw images. Prior methods feathered the seams but did not address their root cause. We handle these problems in a unified framework. We suggest a method for simultaneously estimating the radiometric response and the camera nonuniformity, based on a frame sequence acquired during camera motion. The estimated functions are then compensated for. This permits image mosaicking, in which no seams are apparent. There is no need to resort to dedicated seam-feathering methods. Fundamental ambiguities associated with this estimation problem are stated.

References

S. B. Kang, R. Weiss, “Can we calibrate a camera using an image of a flat, textureless Lambertian surface?” in Proceedings of European Conference on Computer Vision, (Springer, New York, 2000), Part 2, pp. 640–653.

We may avoid the apperance of trivial solution by expressing Eq. (15) in a matrix formulation. This is only one of the possible realizations of the requirement to avoid a nontrivial g. Another possibility is to fix the boundary range values of g.

We placed the filter a few centimeters ahead of the lens. If the filter is placed right next to the lens, it affects the aperture properties[48] without producing spatially varying effects in the image.

S. B. Kang, R. Weiss, “Can we calibrate a camera using an image of a flat, textureless Lambertian surface?” in Proceedings of European Conference on Computer Vision, (Springer, New York, 2000), Part 2, pp. 640–653.

S. B. Kang, R. Weiss, “Can we calibrate a camera using an image of a flat, textureless Lambertian surface?” in Proceedings of European Conference on Computer Vision, (Springer, New York, 2000), Part 2, pp. 640–653.

We may avoid the apperance of trivial solution by expressing Eq. (15) in a matrix formulation. This is only one of the possible realizations of the requirement to avoid a nontrivial g. Another possibility is to fix the boundary range values of g.

We placed the filter a few centimeters ahead of the lens. If the filter is placed right next to the lens, it affects the aperture properties[48] without producing spatially varying effects in the image.

S. B. Kang, R. Weiss, “Can we calibrate a camera using an image of a flat, textureless Lambertian surface?” in Proceedings of European Conference on Computer Vision, (Springer, New York, 2000), Part 2, pp. 640–653.

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